Publications

Federated Learning for Mobility Applications.  MobiSpaces mentioned in acknowledgements. Relevant work for mobility AI & data spaces

The rapidly evolving field of mobility data spaces, integral to the contemporary technological landscape, creates unique challenges and opportunities in the context of legal compliance.

This paper introduces M³fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection.

"Developments in Mobilty Data Science", including recent developments in MobiSpaces

In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been placed on deep learning methodologies for decision-making.

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data.

Effective data governance and management are necessary but challenging prerequisites for creating value from data assets. Findability, accessibility, interoperability, and reusability are guiding principles for data owners in managing and archiving datasets, known as the FAIR Principles.

Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory.

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously.

The increasing prevalence of mobility data in diverse applications such as traffic management requires specialized tools for manipulating it. This paper introduces MEOS (Mobility Engine Open Source), a versatile C library designed explicitly for managing and processing mobility data.

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data.

Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given data set and a speciic machine learning task.